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2019 | OriginalPaper | Buchkapitel

Nova: Diffused Database Processing Using Clouds of Components [Vision Paper]

verfasst von : Shahram Ghandeharizadeh, Haoyu Huang, Hieu Nguyen

Erschienen in: Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis

Verlag: Springer International Publishing

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Abstract

Nova proposes a departure from today’s complex monolithic database management systems (DBMSs) as a service using the cloud. It advocates a server-less alternative consisting of a cloud of simple components that communicate using high speed networks. Nova will monitor the workload of an application continuously, configuring the DBMS to use the appropriate implementation of a component most suitable for processing the workload. In response to load fluctuations, it will adjust the knobs of a component to scale it to meet the performance requirements of the application. The vision of Nova is compelling because it adjusts resource usage, preventing either over-provisioning of resources that sit idle or over-utilized resources that yield a low performance, optimizing total cost of ownership. In addition to introducing Nova, this vision paper presents key research challenges that must be addressed to realize Nova. We explore two challenges in detail.

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Alwagait, E., Ghandeharizadeh, S.: A comparison of alternative web service allocation and scheduling policies. In: 2004 IEEE International Conference on Services Computing, Shanghai, China, pp. 319–326, September 2004. https://doi.org/10.1109/SCC.2004.1358021

Alwagait, E., Ghandeharizadeh, S.: DeW: a dependable web services framework. In: RIDE-WS-ECEG 2004, March, Boston, MA, pp. 111–118 (2004). https://doi.org/10.1109/RIDE.2004.1281710

Annamalai, M., et al.: Sharding the shards: managing datastore locality at scale with akkio. In: OSDI, pp. 445–460. USENIX Association, Carlsbad (2018). https://www.usenix.org/conference/osdi18/presentation/annamalai

Binnig, C., Crotty, A., Galakatos, A., Kraska, T., Zamanian, E.: The end of slow networks: it’s time for a redesign. Proc. VLDB Endow. 9(7), 528–539 (2016). https://doi.org/10.14778/2904483.2904485CrossRef

Bronson, N., Lento, T., Wiener, J.L.: Open data challenges at Facebook. In: 31st IEEE International Conference on Data Engineering, ICDE, Seoul, South Korea, 13–17 April 2015, pp. 1516–1519 (2015)

Cai, Q., et al.: Efficient distributed memory management with RDMA and caching. Proc. VLDB Endow. 11(11), 1604–1617 (2018). https://doi.org/10.14778/3236187.3236209CrossRef

Cao, Z., Tarasov, V., Tiwari, S., Zadok, E.: Towards better understanding of black-box auto-tuning: a comparative analysis for storage systems. In: USENIX Annual Technical Conference, Berkeley, CA, USA, pp. 893–907 (2018). http://dl.acm.org/citation.cfm?id=3277355.3277441

Carey, M.J., et al.: Towards heterogeneous multimedia information systems: the garlic approach. In: RIDE-DOM, pp. 124–131 (1995)

Cattell, R.: Scalable SQL and NoSQL data stores. SIGMOD Rec. 39, 12–27 (2011)CrossRef

10.

Das, S., Li, F., Narasayya, V.R., König, A.C.: Automated demand-driven resource scaling in relational Database-as-a-Service. In: Proceedings of the 2016 International Conference on Management of Data, SIGMOD 2016, pp. 1923–1934. ACM, New York (2016). https://doi.org/10.1145/2882903.2903733

11.

Dewitt, D.J., Ghandeharizadeh, S., Schneider, D.A., Bricker, A., Hsiao, H.-I., Rasmussen, R.: The Gamma database machine project. IEEE Trans. Knowl. Data Eng. 2(1), 44–62 (1990). https://doi.org/10.1109/69.50905CrossRef

12.

Dittrich, K.R., Geppert, A. (eds.): Component Database Systems. Morgan Kaufmann Publishers Inc., San Francisco (2001)

13.

Dong, S., Callaghan, M., Galanis, L., Borthakur, D., Savor, T., Strum, M.: Optimizing space amplification in RocksDB. In: CIDR 2017, 8th Biennial Conference on Innovative Data Systems Research, Chaminade, CA, USA, 8–11 January 2017, Online Proceedings (2017). http://cidrdb.org/cidr2017/papers/p82-dong-cidr17.pdf

14.

Elmore, A., et al.: A demonstration of the BigDAWG polystore system. Proc. VLDB Endow. 8(12), 1908–1911 (2015). https://doi.org/10.14778/2824032.2824098CrossRef

15.

Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15(3), 200–222 (2001)CrossRef

16.

Ghandeharizadeh, S., Yap, J., Nguyen, H.: IQ-Twemcached. http://dblab.usc.edu/users/iq/, https://github.com/scdblab/IQ-Twemcached

17.

Ghandeharizadeh, S., Yap, J., Nguyen, H.: Strong consistency in cache augmented SQL systems. In: Middleware, December 2014

18.

Ghandeharizadeh, S., Nguyen, H.: Design, implementation, and evaluation of write-back policy with cache augmented data stores. Technical report 2018-07, USC Database Lab (2018). Submitted to VLDB 2019: In Revision

19.

Ghemawat, S., Dean, J.: LevelDB. https://github.com/google/leveldb. Accessed 15 Nov 2018

20.

Gray, C., Cheriton, D.: Leases: an efficient fault-tolerant mechanism for distributed file cache consistency. SIGOPS Oper. Syst. Rev. 23(5), 202–210 (1989). https://doi.org/10.1145/74851.74870CrossRef

21.

Kulkarni, C., Kesavan, A., Zhang, T., Ricci, R., Stutsman, R.: Rocksteady: fast migration for low-latency in-memory storage. In: SOSP, pp. 390–405. ACM, New York (2017). https://doi.org/10.1145/3132747.3132784

22.

Lloyd, W., Freedman, M.J., Kaminsky, M., Andersen, D.G.: Don’t settle for eventual: scalable causal consistency for wide-area storage with COPS. In: SOSP, October 2011

23.

Ma, L., Aken, D.V., Hefny, A., Mezerhane, G., Pavlo, A., Gordon, G.J.: Query-based workload forecasting for self-driving database management systems. In: SIGMOD, Houston, TX, USA, 10–15 June 2018, pp. 631–645 (2018). https://doi.org/10.1145/3183713.3196908

24.

Mehdi, S.A., Littley, C., Crooks, N., Alvisi, L., Bronson, N., Lloyd, W.: I can’t believe it’s not causal! Scalable causal consistency with no slowdown cascades. In: NSDI, Boston, MA, USA, 27–29 March 2017, pp. 453–468 (2017). https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/mehdi

25.

Memon, B.N., et al.: RaMP: a lightweight RDMA abstraction for loosely coupled applications. In: 10th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2018). USENIX Association, Boston (2018). https://www.usenix.org/conference/hotcloud18/presentation/memon

26.

O’Neil, P.E., Cheng, E., Gawlick, D., O’Neil, E.J.: The Log-Structured Merge-Tree (LSM-Tree). Acta Inf. 33(4), 351–385 (1996). https://doi.org/10.1007/s002360050048CrossRefMATH

27.

O’Neil, P.E., Quass, D.: Improved query performance with variant indexes. In: SIGMOD, Tucson, Arizona, USA, 13–15 May 1997, pp. 38–49 (1997). https://doi.org/10.1145/253260.253268

28.

Ousterhout, J., et al.: The RAMCloud storage system. ACM Trans. Comput. Syst. 33(3), 7:1–7:55 (2015). https://doi.org/10.1145/2806887CrossRef

29.

Pavlo, A., et al.: Self-driving database management systems. In: CIDR 2017, Chaminade, CA, USA, 8–11 January 2017, Online Proceedings (2017). http://cidrdb.org/cidr2017/papers/p42-pavlo-cidr17.pdf

30.

Płuciennik, E., Zgorzałek, K.: The multi-model databases – a review. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2017. CCIS, vol. 716, pp. 141–152. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58274-0_12CrossRef

31.

Raab, F., Kohler, W., Shah, A.: Overview of the TPC-C benchmark. http://www.tpc.org/tpcc//detail.asp. Accessed 15 Nov 2018

32.

Seltzer, M.: Beyond relational databases. Commun. ACM 51(7), 52–58 (2008). https://doi.org/10.1145/1364782.1364797CrossRef

33.

Shan, Y., Huang, Y., Chen, Y., Zhang, Y.: LegoOS: a disseminated, distributed OS for hardware resource disaggregation. In: OSDI, pp. 69–87. USENIX Association, Carlsbad (2018). https://www.usenix.org/conference/osdi18/presentation/shan

34.

Smolinski, M.: Impact of storage space configuration on transaction processing performance for relational database in PostgreSQL. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2018. CCIS, vol. 928, pp. 157–167. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99987-6_12CrossRef

35.

Stonebraker, M., et al.: C-Store: a column-oriented DBMS. In: VLDB, Trondheim, Norway, 30 August–2 September 2005, pp. 553–564 (2005). http://www.vldb.org/archives/website/2005/program/paper/thu/p553-stonebraker.pdf

36.

Tsakalozos, K., Verroios, V., Roussopoulos, M., Delis, A.: Live VM migration under time-constraints in share-nothing IaaS-clouds. IEEE Trans. Parallel Distrib. Syst. 28(8), 2285–2298 (2017). https://doi.org/10.1109/TPDS.2017.2658572CrossRef

37.

Van Aken, D., Pavlo, A., Gordon, G.J., Zhang, B.: Automatic database management system tuning through large-scale machine learning. In: SIGMOD, pp. 1009–1024. ACM, New York (2017). https://doi.org/10.1145/3035918.3064029

38.

Wang, S., Li, C., Hoffmann, H., Lu, S., Sentosa, W., Kistijantoro, A.I.: Understanding and auto-adjusting performance-sensitive configurations. In: ASPLOS, pp. 154–168. ACM, New York (2018). https://doi.org/10.1145/3173162.3173206

39.

White, B., et al.: An integrated experimental environment for distributed systems and networks. In: OSDI, December 2002

40.

Zamanian, E., Binnig, C., Harris, T., Kraska, T.: The end of a myth: distributed transactions can scale. Proc. VLDB Endow. 10(6), 685–696 (2017). https://doi.org/10.14778/3055330.3055335CrossRef

Titel: Nova: Diffused Database Processing Using Clouds of Components [Vision Paper]
verfasst von: Shahram Ghandeharizadeh
Haoyu Huang
Hieu Nguyen
Verlag: Springer International Publishing
Buch: Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis
Print ISBN: 978-3-030-19092-7

Electronic ISBN: 978-3-030-19093-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-19093-4_1

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